Nonparametric estimation of returns to scale using input distance functions: an application to large US banks
We derive new measures of returns to scale based on input distance functions (IDFs) and estimate them using nonparametric regression methods. In contrast to the cost function approach, the IDF does not require input prices which are usually unavailable or measured imprecisely. In addition, we can ac...
- Autores:
-
Restrepo-Tobón, Diego
Kumbhakar, Subal C.
- Tipo de recurso:
- Fecha de publicación:
- 2015
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/7613
- Acceso en línea:
- http://hdl.handle.net/10784/7613
- Palabra clave:
- Nonparametric regression
Returns to scale
Distance functions
Banks
- Rights
- License
- restrictedAccess
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20152015-11-06T21:15:34Z20152015-11-06T21:15:34Z0377-7332http://hdl.handle.net/10784/761310.1007/s00181-014-0831-9We derive new measures of returns to scale based on input distance functions (IDFs) and estimate them using nonparametric regression methods. In contrast to the cost function approach, the IDF does not require input prices which are usually unavailable or measured imprecisely. In addition, we can account for equity and physical capital in the IDF. These variables are either excluded from the analysis (especially in a cost function approach) or treated as quasi-fixed inputs, because their prices are not readily available. In our application, we use data for bank holding companies and large commercial banks in the U.S. from 2000 to 2010. We find that although some of these institutions enjoy increasing returns to scale, scale economies are economically small. Thus, concerns about potential cost increases arising from breaking up large banking organizations seem exaggerated, especially from the scale economies point of view.engSpringer International Publishing Empirical Economics. Vol. 48, (1), 2015, pp.143-168http://link.springer.com/article/10.1007/s00181-014-0831-9http://link.springer.com/article/10.1007/s00181-014-0831-9restrictedAccess© Springer International Publishing AG, Part of Springer Science+Business MediaAcceso restringidohttp://purl.org/coar/access_right/c_16ecEmpirical Economics. Vol. 48, (1), 2015, pp.143-168Nonparametric estimation of returns to scale using input distance functions: an application to large US banksarticleinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículoObra publicadapublishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Nonparametric regressionReturns to scaleDistance functionsBanksEconomía y FinanzasFinanzasRestrepo-Tobón, DiegoKumbhakar, Subal C.EAFIT UniversityBinghamton UniversityGrupo de Investigación Finanzas y BancaEmpirical Economics481143168ORIGINALs00181-014-0831-9.pdfs00181-014-0831-9.pdfapplication/pdf373513https://repository.eafit.edu.co/bitstreams/4781c051-09d1-48b4-a564-c051efcb5861/download9834acc3ca934972ed37ac4e76bf24cbMD5110784/7613oai:repository.eafit.edu.co:10784/76132023-03-15 08:19:10.923open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co |
dc.title.eng.fl_str_mv |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
title |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
spellingShingle |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks Nonparametric regression Returns to scale Distance functions Banks |
title_short |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
title_full |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
title_fullStr |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
title_full_unstemmed |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
title_sort |
Nonparametric estimation of returns to scale using input distance functions: an application to large US banks |
dc.creator.fl_str_mv |
Restrepo-Tobón, Diego Kumbhakar, Subal C. |
dc.contributor.department.spa.fl_str_mv |
Economía y Finanzas Finanzas |
dc.contributor.author.spa.fl_str_mv |
Restrepo-Tobón, Diego Kumbhakar, Subal C. |
dc.contributor.affiliation.spa.fl_str_mv |
EAFIT University Binghamton University |
dc.contributor.program.spa.fl_str_mv |
Grupo de Investigación Finanzas y Banca |
dc.subject.keyword.eng.fl_str_mv |
Nonparametric regression Returns to scale Distance functions Banks |
topic |
Nonparametric regression Returns to scale Distance functions Banks |
description |
We derive new measures of returns to scale based on input distance functions (IDFs) and estimate them using nonparametric regression methods. In contrast to the cost function approach, the IDF does not require input prices which are usually unavailable or measured imprecisely. In addition, we can account for equity and physical capital in the IDF. These variables are either excluded from the analysis (especially in a cost function approach) or treated as quasi-fixed inputs, because their prices are not readily available. In our application, we use data for bank holding companies and large commercial banks in the U.S. from 2000 to 2010. We find that although some of these institutions enjoy increasing returns to scale, scale economies are economically small. Thus, concerns about potential cost increases arising from breaking up large banking organizations seem exaggerated, especially from the scale economies point of view. |
publishDate |
2015 |
dc.date.available.none.fl_str_mv |
2015-11-06T21:15:34Z |
dc.date.issued.none.fl_str_mv |
2015 |
dc.date.accessioned.none.fl_str_mv |
2015-11-06T21:15:34Z |
dc.date.none.fl_str_mv |
2015 |
dc.type.eng.fl_str_mv |
article info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.local.spa.fl_str_mv |
Artículo |
dc.type.hasVersion.spa.fl_str_mv |
Obra publicada |
dc.type.hasVersion.eng.fl_str_mv |
publishedVersion |
status_str |
publishedVersion |
dc.identifier.issn.none.fl_str_mv |
0377-7332 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10784/7613 |
dc.identifier.doi.none.fl_str_mv |
10.1007/s00181-014-0831-9 |
identifier_str_mv |
0377-7332 10.1007/s00181-014-0831-9 |
url |
http://hdl.handle.net/10784/7613 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.spa.fl_str_mv |
Empirical Economics. Vol. 48, (1), 2015, pp.143-168 |
dc.relation.isversionof.none.fl_str_mv |
http://link.springer.com/article/10.1007/s00181-014-0831-9 |
dc.relation.uri.none.fl_str_mv |
http://link.springer.com/article/10.1007/s00181-014-0831-9 |
dc.rights.eng.fl_str_mv |
restrictedAccess |
dc.rights.spa.fl_str_mv |
© Springer International Publishing AG, Part of Springer Science+Business Media |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.local.spa.fl_str_mv |
Acceso restringido |
rights_invalid_str_mv |
restrictedAccess © Springer International Publishing AG, Part of Springer Science+Business Media Acceso restringido http://purl.org/coar/access_right/c_16ec |
dc.publisher.eng.fl_str_mv |
Springer International Publishing |
dc.source.spa.fl_str_mv |
Empirical Economics. Vol. 48, (1), 2015, pp.143-168 |
institution |
Universidad EAFIT |
bitstream.url.fl_str_mv |
https://repository.eafit.edu.co/bitstreams/4781c051-09d1-48b4-a564-c051efcb5861/download |
bitstream.checksum.fl_str_mv |
9834acc3ca934972ed37ac4e76bf24cb |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad EAFIT |
repository.mail.fl_str_mv |
repositorio@eafit.edu.co |
_version_ |
1814110126895792128 |